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Current State-of-the-Art Spectroscopy and Chemometrics Techniques in Food Authentication and...
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Current State-of-the-Art Spectroscopy and Chemometrics Techniques in Food Authentication and Quality Assessment

A special issue of Foods (ISSN 2304-8158). This special issue belongs to the section "Food Analytical Methods".

Deadline for manuscript submissions: closed (10 June 2023) | Viewed by 6011

Special Issue Editor

Dr. Banu ÖZEN

E-Mail Website
Guest Editor
Department of Food Engineering, Izmir Institute of Technology, Urla-Izmir, Turkey
Interests: authentication; adulteration; infrared spectroscopy; chemometrics; edible oils

Special Issue Information

Dear Colleagues,

Authenticity problems are more likely to arise in the food industry as a result of recent sharp price increases in food and rising consumer interest in more nutritious and authentic food options. In general, spectroscopic methods are relatively rapid, non-destructive, and produce less chemical waste. These methods have been used quite successfully in detecting various types of food fraud. Another area in which these spectroscopic methods have found use is the monitoring of the quality parameters of food products and food processes. Spectroscopic methods generate large amounts of data that are densely packed with information. Chemometric methods provide the evaluation of these data and generate classification and prediction models, which are quite useful for authentication and quality monitoring of food products. The state-of-the-art applications of spectroscopic methods in conjunction with chemometric methods for food products is a research field that is constantly evolving due to the tireless efforts of fraudsters in discovering new ways to deceive.

Dr. Banu Özen
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Foods is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • authentication
  • adulteration
  • spectroscopy
  • chemometrics
  • food quality

Published Papers (4 papers)

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Research

17 pages, 3919 KiB  
Article
A Self-Supervised Anomaly Detector of Fruits Based on Hyperspectral Imaging
by Yisen Liu, Songbin Zhou, Zhiyong Wan, Zefan Qiu, Lulu Zhao, Kunkun Pang, Chang Li and Zexuan Yin
Foods 2023, 12(14), 2669; https://doi.org/10.3390/foods12142669 - 11 Jul 2023
Cited by 1 | Viewed by 1147
Abstract
Hyperspectral imaging combined with chemometric approaches is proven to be a powerful tool for the quality evaluation and control of fruits. In fruit defect-detection scenarios, developing an unsupervised anomaly detection framework is vital, as defect sample preparation is labor-intensive and time-consuming, especially for [...] Read more.
Hyperspectral imaging combined with chemometric approaches is proven to be a powerful tool for the quality evaluation and control of fruits. In fruit defect-detection scenarios, developing an unsupervised anomaly detection framework is vital, as defect sample preparation is labor-intensive and time-consuming, especially for exploring potential defects. In this paper, a spectral–spatial, information-based, self-supervised anomaly detection (SSAD) approach is proposed. During training, an auxiliary classifier is proposed to identify the projection axes of principal component (PC) images that were transformed from the hyperspectral data cubes. In test time, the fully connected layer of the learned classifier was used as a ‘spectral–spatial’ feature extractor, and the feature similarity metric was adopted as the score function for the downstream anomaly evaluation task. The proposed network was evaluated with two fruit data sets: a strawberry data set with bruised, infected, chilling-injured, and contaminated test samples and a blueberry data set with bruised, infected, chilling-injured, and wrinkled samples as anomalies. The results show that the SSAD yielded the best anomaly detection performance (AUC = 0.923 on average) over the baseline methods, and the visualization results further confirmed its advantage in extracting effective ‘spectral–spatial’ latent representation. Moreover, the robustness of SSAD is verified with the data pollution experiment; it performed significantly better than the baselines when a portion of anomalous samples was involved in the training process. Full article
(This article belongs to the Special Issue Current State-of-the-Art Spectroscopy and Chemometrics Techniques in Food Authentication and Quality Assessment)
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17 pages, 2964 KiB  
Article
Design of a Novel Auxiliary Diagnostic Test for the Determination of Authenticity of Tequila 100% Agave Silver Class Based on Chemometrics Analysis of the Isotopic Fingerprint of the Beverage
by Rocío Fonseca-Aguiñaga, Uriel E. Navarro-Arteaga, Martin Muñoz-Sánchez, Humberto Gómez-Ruiz, Walter M. Warren-Vega and Luis A. Romero-Cano
Foods 2023, 12(13), 2605; https://doi.org/10.3390/foods12132605 - 05 Jul 2023
Cited by 2 | Viewed by 1411
Abstract
The present research shows a robust isotopic ratio characterization of Carbon-13 (δ13CVPDB) in congeneric compounds such as methanol, n-propanol, isoamyl alcohol, ethyl lactate, ethyl acetate, ethanol, and acetaldehyde in representative samples (n = 69) of Tequila 100% agave [...] Read more.
The present research shows a robust isotopic ratio characterization of Carbon-13 (δ13CVPDB) in congeneric compounds such as methanol, n-propanol, isoamyl alcohol, ethyl lactate, ethyl acetate, ethanol, and acetaldehyde in representative samples (n = 69) of Tequila 100% agave silver class (TSC), employing gas chromatography/combustion/isotope-ratio mass spectrometry (GC/C/IRMS). From the information obtained, the construction of a radial plot attributable to the isotopic fingerprint of TSC was achieved. With this information, a diagnostic test was designed to determine the authenticity of TSC, comparing alcoholic beverages from other agave species as non-authentic samples. The sensitivity of the test was 94.2%; the specificity was 83.3%. Additionally, non-authentic samples were analyzed that meet all the criteria established in the regulations. The results obtained show that the GC/C/IRMS analytical technique and designed diagnostic test are useful as auxiliary parameters to determine the authenticity of the beverage, thus managing to determine the adulteration or falsification of the product. Full article
(This article belongs to the Special Issue Current State-of-the-Art Spectroscopy and Chemometrics Techniques in Food Authentication and Quality Assessment)
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12 pages, 1492 KiB  
Article
Texture Feature Extraction from 1H NMR Spectra for the Geographical Origin Traceability of Chinese Yam
by Zhongyi Hu, Zhenzhen Luo, Yanli Wang, Qiuju Zhou, Shuangyan Liu and Qiang Wang
Foods 2023, 12(13), 2476; https://doi.org/10.3390/foods12132476 - 24 Jun 2023
Cited by 1 | Viewed by 1027
Abstract
Adulteration is widespread in the herbal and food industry and seriously restricts traditional Chinese medicine development. Accurate identification of geo-authentic herbs ensures drug safety and effectiveness. In this study, 1H NMR combined intelligent “rotation-invariant uniform local binary pattern” identification was implemented for [...] Read more.
Adulteration is widespread in the herbal and food industry and seriously restricts traditional Chinese medicine development. Accurate identification of geo-authentic herbs ensures drug safety and effectiveness. In this study, 1H NMR combined intelligent “rotation-invariant uniform local binary pattern” identification was implemented for the geographical origin confirmation of geo-authentic Chinese yam (grown in Jiaozuo, Henan province) from Chinese yams grown in other locations. Our results showed that the texture feature of 1H NMR image extracted with rotation-invariant uniform local binary pattern for identification is far superior compared to the original NMR data. Furthermore, data preprocessing is necessary. Moreover, the model combining a feature extraction algorithm and support vector machine (SVM) classifier demonstrated good robustness. This approach is advantageous, as it is accurate, rapid, simple, and inexpensive. It is also suitable for the geographical origin traceability of other geographical indication agricultural products. Full article
(This article belongs to the Special Issue Current State-of-the-Art Spectroscopy and Chemometrics Techniques in Food Authentication and Quality Assessment)
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13 pages, 3929 KiB  
Article
Applications of UV–Visible, Fluorescence and Mid-Infrared Spectroscopic Methods Combined with Chemometrics for the Authentication of Apple Vinegar
by Cagri Cavdaroglu and Banu Ozen
Foods 2023, 12(6), 1139; https://doi.org/10.3390/foods12061139 - 08 Mar 2023
Cited by 3 | Viewed by 1858
Abstract
Spectroscopic techniques as untargeted methods have great potential in food authentication studies, and the evaluation of spectroscopic data with chemometric methods can provide accurate predictions of adulteration even for hard-to-identify cases such as the mixing of vinegar with adulterants having a very similar [...] Read more.
Spectroscopic techniques as untargeted methods have great potential in food authentication studies, and the evaluation of spectroscopic data with chemometric methods can provide accurate predictions of adulteration even for hard-to-identify cases such as the mixing of vinegar with adulterants having a very similar chemical nature. In this study, we aimed to compare the performances of three spectroscopic methods (fluorescence, UV–visible, mid-infrared) in the detection of acetic-acid/apple-vinegar and spirit-vinegar/apple-vinegar mixtures (1–50%). Data obtained with the three spectroscopic techniques were used in the generation of classification models with partial least square discriminant analysis (PLS-DA) and orthogonal partial least square discriminant analysis (OPLS-DA) to differentiate authentic and mixed samples. An improved classification approach was used in choosing the best models through a number of calibration and validation sets. Only the mid-infrared data provided robust and accurate classification models with a high classification rate (up to 96%), sensitivity (1) and specificity (up to 0.96) for the differentiation of the adulterated samples from authentic apple vinegars. Therefore, it was concluded that mid-infrared spectroscopy is a useful tool for the rapid authentication of apple vinegars and it is essential to test classification models with different datasets to obtain a robust model. Full article
(This article belongs to the Special Issue Current State-of-the-Art Spectroscopy and Chemometrics Techniques in Food Authentication and Quality Assessment)
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